The numerical values in Figure 2 shows the AP for each of the training and evaluation conditions. The first column shows that training on the KITTI dataset achieves a high performance level, but generalizes poorly to the BDD and CITYSCAPE data sets. The second column shows that BDD training generalizes well to the other datasets. The third column shows that CITYSCAPE generalizes to KITTI well, but does not generalize well to BDD. A few reports of generalization scores in the literature are consistent with these values. In one study, training on 70,000 BDD images achieved a generalization to KITTI of about 0.55 using a criterion of AP@0.7IOU [1]}. With 3,000 BDD images, we found a generalization performance level of 0.67 at AP@0.5IOU.
